Abstract: The invention provides an ETL pipeline system including an interface configured to obtain a plurality of graph configuration components. Each graph configuration component includes information representative of one or more computational logic rules. The system further includes a computation graph generator configured to generate a computation graph based on the obtained graph configuration components. The generated computation graph includes a node for each graph configuration component and one or more links representative of relationships between the nodes. The system further includes a computation graph adaptor configured to receive, from an external source, external information relating to the graph configuration components, and to adapt the generated computation graph based on the external information. The system further includes a computation graph runner configured to run the adapted computation graph.

Abstract: Extracting entities from a document with a hierarchical entity graph of entities. Entity definitions and entity recognition definitions are customized by a user and provided. The configuration information is utilized to generate (905) an entity graph, which is then utilized to parse one or more documents. In some implementations, the resulting parse tree may be utilized, in conjunction with user feedback, to generate one or more training instances for a machine learning model assigned to one or more of the custom nodes as an entity recognition definition. Parsing of the resulting tree may be performed with a lazy parsing methodology, with only the portions of interest to the user being identified in the document.

Abstract: Extracting entities from a document with a hierarchical entity graph of entities. Entity definitions and entity recognition definitions are customized by a user and provided. The configuration information is utilized to generate (905) an entity graph, which is then utilized to parse one or more documents. In some implementations, the resulting parse tree may be utilized, in conjunction with user feedback, to generate one or more training instances for a machine learning model assigned to one or more of the custom nodes as an entity recognition definition. Parsing of the resulting tree may be performed with a lazy parsing methodology, with only the portions of interest to the user being identified in the document.